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- W2050573622 abstract "Hopfield-type thermodynamic networks composed of functionally homogeneous visible units have been applied to a variety of structurally simple NP-hard optimization problems. A fundamental obstacle to the application of neural networks to difficult problems is that these problems must first be reduced to 0-1 Hamiltonian minimization problems. It has been shown that certain optimization problems cannot be embedded in networks composed entirely of visible units. A method for defining necessary hidden units together with their best features is presented. A knapsack-packing network of O ( n ) units with standard and conjunctive synapses is derived, and simulation results are presented" @default.
- W2050573622 created "2016-06-24" @default.
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- W2050573622 date "1990-01-01" @default.
- W2050573622 modified "2023-09-25" @default.
- W2050573622 title "The definition of necessary hidden units in neural networks for combinatorial optimization" @default.
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- W2050573622 doi "https://doi.org/10.1109/ijcnn.1990.137931" @default.
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